Parallel CNN-Deep Learning Clinical-Imaging Signature for Assessing Pathologic Grade and Prognosis of Soft Tissue Sarcoma Patients.

BACKGROUND: Traditional biopsies pose risks and may not accurately reflect soft tissue sarcoma (STS) heterogeneity. MRI provides a noninvasive, comprehensive alternative. PURPOSE: To assess the diagnostic accuracy of histological grading and prognosis in STS patients when integrating clinical-imaging parameters with deep learning (DL) features from preoperative MR images. STUDY TYPE: Retrospective/prospective. POPULATION: 354 pathologically confirmed STS patients (226 low-grade, 128 high-grade) from three hospitals and the Cancer Imaging Archive (TCIA), divided into training (n = 185), external test (n = 125), and TCIA cohorts (n = 44). 12 patients (6 low-grade, 6 high-grade) were enrolled into prospective validation cohort. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T/Unenhanced T1-weighted and fat-suppressed-T2-weighted. ASSESSMENT: DL features were extracted from MR images using a parallel ResNet-18 model to construct DL signature. Clinical-imaging characteristics included age, gender, tumor-node-metastasis stage and MRI semantic features (depth, number, heterogeneity at T1WI/FS-T2WI, necrosis, and peritumoral edema). Logistic regression analysis identified significant risk factors for the clinical model. A DL clinical-imaging signature (DLCS) was constructed by incorporating DL signature with risk factors, evaluated for risk stratification, and assessed for progression-free survival (PFS) in retrospective cohorts, with an average follow-up of 23 ± 22 months. STATISTICAL TESTS: Logistic regression, Cox regression, Kaplan-Meier curves, log-rank test, area under the receiver operating characteristic curve (AUC)，and decision curve analysis. A P-value <0.05 was considered significant. RESULTS: The AUC values for DLCS in the external test, TCIA, and prospective test cohorts (0.834, 0.838, 0.819) were superior to clinical model (0.662, 0.685, 0.694). Decision curve analysis showed that the DLCS model provided greater clinical net benefit over the DL and clinical models. Also, the DLCS model was able to risk-stratify patients and assess PFS. DATA CONCLUSION: The DLCS exhibited strong capabilities in histological grading and prognosis assessment for STS patients, and may have potential to aid in the formulation of personalized treatment plans. TECHNICAL EFFICACY: Stage 2.

Small Transjugular Intrahepatic Portosystemic Shunt Plus Variceal Embolization for Gastric Varices: A Multicenter Cohort Study.

BACKGROUND & AIMS: The effect of transjugular intrahepatic portosystemic shunt (TIPS) plus variceal embolization for treating gastric varices (GVs) remains controversial. This nationwide multicenter cohort study aimed to evaluate whether adding variceal embolization to a small diameter (8-mm) TIPS could reduce the rebleeding incidence in patients with different types of GVs. METHODS: This retrospective cohort study involved 629 patients who underwent 8-mm TIPS for gastric varices at 7 medical centers. The primary endpoint was all-cause rebleeding, and the secondary endpoints included overt hepatic encephalopathy (OHE) and all-cause mortality. RESULTS: A total of 629 patients were included. Among them, 429 (68.2%) had gastroesophageal varices type 1 (GOV1), 145 (23.1%) had gastroesophageal varices type 2 (GOV2), and 55 (8.7%) had isolated gastric varices type 1 (IGV1). In the entire cohort, adjunctive embolization reduced rebleeding (6.2% vs 13.6%; P = .005) and OHE (31.0% vs 39.4%; P = .02) compared with TIPS alone. However, no significant differences were found in mortality (12.0% vs 9.7%; P = .42). In patients with GOV2 and IGV1, TIPS plus variceal embolization reduced both rebleeding (GOV2: 7.8% vs 25.1%; P = .01; IGV1: 5.6% vs 30.8%; P = .03) and OHE (GOV2: 31.8% vs 51.5%; P = .008; IGV1: 11.6% vs 38.5%; P = .04). However, in patients with GOV1, adjunctive embolization did not reduce rebleeding (5.9% vs 8.7%; P = .37) or OHE (33.1% vs 35.3%; P = .60). CONCLUSIONS: Compared with TIPS alone, 8-mm TIPS plus variceal embolization reduced rebleeding and OHE in patients with GOV2 and IGV1. These findings suggest that patients with GOV2 and IGV1, rather than GOV1, could benefit from embolization with TIPS.

Deep learning-based platform performs high detection sensitivity of intracranial aneurysms in 3D brain TOF-MRA: An external clinical validation study.

PURPOSE: To evaluate the diagnostic efficacy of a developed artificial intelligence (AI) platform incorporating deep learning algorithms for the automated detection of intracranial aneurysms in time-of-flight (TOF) magnetic resonance angiography (MRA). METHOD: This retrospective study encompassed 3D TOF MRA images acquired between January 2023 and June 2023, aiming to validate the presence of intracranial aneurysms via our developed AI platform. The manual segmentation results by experienced neuroradiologists served as the "gold standard". Following annotation of MRA images by neuroradiologists using InferScholar software, the AI platform conducted automatic segmentation of intracranial aneurysms. Various metrics including accuracy (ACC), balanced ACC, area under the curve (AUC), sensitivity (SE), specificity (SP), F1 score, Brier Score, and Net Benefit were utilized to evaluate the generalization of AI platform. Comparison of intracranial aneurysm identification performance was conducted between the AI platform and six radiologists with experience ranging from 3 to 12 years in interpreting MR images. Additionally, a comparative analysis was carried out between radiologists' detection performance based on independent visual diagnosis and AI-assisted diagnosis. Subgroup analyses were also performed based on the size and location of the aneurysms to explore factors impacting aneurysm detectability. RESULTS: 510 patients were enrolled including 215 patients (42.16 %) with intracranial aneurysms and 295 patients (57.84 %) without aneurysms. Compared with six radiologists, the AI platform showed competitive discrimination power (AUC, 0.96), acceptable calibration (Brier Score loss, 0.08), and clinical utility (Net Benefit, 86.96 %). The AI platform demonstrated superior performance in detecting aneurysms with an overall SE, SP, ACC, balanced ACC, and F1 score of 91.63 %, 92.20 %, 91.96 %, 91.92 %, and 90.57 % respectively, outperforming the detectability of the two resident radiologists. For subgroup analysis based on aneurysm size and location, we observed that the SE of the AI platform for identifying tiny (diameter＜3mm), small (3 mm ≤ diameter＜5mm), medium (5 mm ≤ diameter＜7mm) and large aneurysms (diameter ≥ 7 mm) was 87.80 %, 93.14 %, 95.45 %, and 100 %, respectively. Furthermore, the SE for detecting aneurysms in the anterior circulation was higher than that in the posterior circulation. Utilizing the AI assistance, six radiologists (i.e., two residents, two attendings and two professors) achieved statistically significant improvements in mean SE (residents: 71.40 % vs. 88.37 %; attendings: 82.79 % vs. 93.26 %; professors: 90.07 % vs. 97.44 %; P < 0.05) and ACC (residents: 85.29 % vs. 94.12 %; attendings: 91.76 % vs. 97.06 %; professors: 95.29 % vs. 98.82 %; P < 0.05) while no statistically significant change was observed in SP. Overall, radiologists' mean SE increased by 11.40 %, mean SP increased by 1.86 %, and mean ACC increased by 5.88 %, mean balanced ACC promoted by 6.63 %, mean F1 score grew by 7.89 %, and Net Benefit rose by 12.52 %, with a concurrent decrease in mean Brier score declined by 0.06. CONCLUSIONS: The deep learning algorithms implemented in the AI platform effectively detected intracranial aneurysms on TOF-MRA and notably enhanced the diagnostic capabilities of radiologists. This indicates that the AI-based auxiliary diagnosis model can provide dependable and precise prediction to improve the diagnostic capacity of radiologists.

Risk factors for suspected pulmonary embolism in children: Complication of Mycoplasma pneumoniae pneumonia.

PURPOSE: Pulmonary embolism (PE) is not a rare complication of Mycoplasma pneumoniae pneumonia (MPP) in children. We sought to determine the incidence of PE in children with MPP who underwent clinically indicated CT pulmonary angiography (CTPA) and to evaluate the risk factors for PE. METHODS: All 106 children with MPP who were clinically suspected of having PE and who underwent CTPA were retrospectively enrolled from June 2018 to December 2021. The clinical features, laboratory data, and radiological parameters were recorded (e.g., lung consolidation involved and the Qanadli score). A Cox proportional hazards model and area under the receiver operating characteristic (ROC) curve were used to evaluate the risk factors and prognostic discriminatory capacity for PE. RESULTS: PE was detected in 26 of 106 (24.5 %) children (mean age, 6.2 years ± 3.3 years; 53 boys). Sixteen of the 26 (61.5 %) children with PE were boys. The mean age of the children with PE was 8.1 ± 2.9 years, and the mean Qanadli score was 15.3 ± 10.2. Children with PE had higher D-dimer levels (9.3 ± 7.1 mg/Lvs. 3.6 ± 3.8 mg/L) and a greater frequency of lung lobe consolidation (25 (96.2 %) vs. 64 (80.0 %)) (all P < 0.05). For children with MPP, age (hazard ratio (HR) = 1.96 (95 % CI1.04, 3.71; P = 0.037), D-dimer level (HR = 1.52, 95 % CI: 1.03, 2.24; P = 0.029), and bilateral lung consolidation (HR = 2.41, 95 % CI: 1.03, 5.58; P = 0.043) were found to be independent predictors of PE. CONCLUSION: Clinical and CT radiological predictors could be used to predict PE in children with MPP. The use of risk factor assessment as a tool has the potential to guide more appropriate use of CTPA in children.

Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma.

BACKGROUND: To develop a magnetic resonance imaging (MRI)-based radiomics signature for evaluating the risk of soft tissue sarcoma (STS) disease progression. METHODS: We retrospectively enrolled 335 patients with STS (training, validation, and The Cancer Imaging Archive sets, n = 168, n = 123, and n = 44, respectively) who underwent surgical resection. Regions of interest were manually delineated using two MRI sequences. Among 12 machine learning-predicted signatures, the best signature was selected, and its prediction score was inputted into Cox regression analysis to build the radiomics signature. A nomogram was created by combining the radiomics signature with a clinical model constructed using MRI and clinical features. Progression-free survival was analyzed in all patients. We assessed performance and clinical utility of the models with reference to the time-dependent receiver operating characteristic curve, area under the curve, concordance index, integrated Brier score, decision curve analysis. RESULTS: For the combined features subset, the minimum redundancy maximum relevance-least absolute shrinkage and selection operator regression algorithm + decision tree classifier had the best prediction performance. The radiomics signature based on the optimal machine learning-predicted signature, and built using Cox regression analysis, had greater prognostic capability and lower error than the nomogram and clinical model (concordance index, 0.758 and 0.812; area under the curve, 0.724 and 0.757; integrated Brier score, 0.080 and 0.143, in the validation and The Cancer Imaging Archive sets, respectively). The optimal cutoff was - 0.03 and cumulative risk rates were calculated. DATA CONCLUSION: To assess the risk of STS progression, the radiomics signature may have better prognostic power than a nomogram/clinical model.

Preoperative CT-based deep learning radiomics model to predict lymph node metastasis and patient prognosis in bladder cancer: a two-center study.

OBJECTIVE: To establish a model for predicting lymph node metastasis in bladder cancer (BCa) patients. METHODS: We retroactively enrolled 239 patients who underwent three-phase CT and resection for BCa in two centers (training set, n = 185; external test set, n = 54). We reviewed the clinical characteristics and CT features to identify significant predictors to construct a clinical model. We extracted the hand-crafted radiomics features and deep learning features of the lesions. We used the Minimum Redundancy Maximum Relevance algorithm and the least absolute shrinkage and selection operator logistic regression algorithm to screen features. We used nine classifiers to establish the radiomics machine learning signatures. To compensate for the uneven distribution of the data, we used the synthetic minority over-sampling technique to retrain each machine-learning classifier. We constructed the combined model using the top-performing radiomics signature and clinical model, and finally presented as a nomogram. We evaluated the combined model's performance using the area under the receiver operating characteristic, accuracy, calibration curves, and decision curve analysis. We used the Kaplan-Meier survival curve to analyze the prognosis of BCa patients. RESULTS: The combined model incorporating radiomics signature and clinical model achieved an area under the receiver operating characteristic of 0.834 (95% CI: 0.659-1.000) for the external test set. The calibration curves and decision curve analysis demonstrated exceptional calibration and promising clinical use. The combined model showed good risk stratification performance for progression-free survival. CONCLUSION: The proposed CT-based combined model is effective and reliable for predicting lymph node status of BCa patients preoperatively. CRITICAL RELEVANCE STATEMENT: Bladder cancer is a type of urogenital cancer that has a high morbidity and mortality rate. Lymph node metastasis is an independent risk factor for death in bladder cancer patients. This study aimed to investigate the performance of a deep learning radiomics model for preoperatively predicting lymph node metastasis in bladder cancer patients. KEY POINTS: • Conventional imaging is not sufficiently accurate to determine lymph node status. • Deep learning radiomics model accurately predicted bladder cancer lymph node metastasis. • The proposed method showed satisfactory patient risk stratification for progression-free survival.

Factors Associated with Dialysis Initiation in Patients with Predialysis Arteriovenous Fistula.

INTRODUCTION: A large proportion of patients initiated hemodialysis with a central vein catheter rather than a permanent vascular access which was recommended by guidelines. One major barrier was the paucity of evidence regarding the optimal timing of vascular access creation in predialysis patients. METHODS: Our study prospectively enrolled 300 patients undergoing predialysis arteriovenous fistula (AVF) creation in our center from 2015 to 2018. Cox proportional hazard regression was performed to identify which demographic and clinical factors were associated with the initiation of hemodialysis after AVF surgery. A receiver operating characteristic area under the curve (AUC) was used to assess the predictive power of preoperative factors for the likelihood of hemodialysis initiation. RESULTS: Overall, 163 (54.3%), 214 (71.3%), and 275 (91.7%) patients initiated hemodialysis within 3 months, 6 months, and 1 year, respectively, after AVF creation. The median time between AVF creation and hemodialysis start was 71.5 days. Using multivariate Cox regression analysis, three factors were associated with hemodialysis initiation within 1 year: serum phosphorus (HR = 1.407, p = 0.021), diabetic kidney disease (DKD) (HR = 1.429, p = 0.039), and cystatin C (HR = 1.179, p = 0.009). Cystatin C alone had a moderate predictive value for dialysis initiation (AUC = 0.746; p < 0.001), whereas the full model had a higher predictive value (AUC = 0.800; p < 0.001). CONCLUSION: DKD, serum cystatin C, and phosphorus at access surgery were associated with hemodialysis initiation within 1 year of the predialysis AVF creation. Our findings provide a basis for a more customized approach to planning AVF placement in patients with chronic kidney disease.

Deep learning signature based on multiphase enhanced CT for bladder cancer recurrence prediction: a multi-center study.

Background: Accurate stratification of recurrence risk for bladder cancer (BCa) is essential for precise individualized therapy. This study aimed to develop and validate a model for predicting the risk of recurrence in BCa patients postoperatively using 3-phase enhanced CT images. Methods: We retrospectively enrolled 874 BCa patients across four centers between January 2006 and December 2021. Patients from one center were used as training set, while the remaining patients went into the validation set. We trained a deep learning (DL) model based on convolutional neural networks using 3-phase enhanced CT images. The resulting prediction scores were entered into Cox regression analysis to obtain DL scores and construct a DL signature. DL scores and clinical features were then used as deep learning radioclinical signature. The predictive performance of DL signature was assessed according to concordance index and area under curve compared with deep learning radioclinical signature, clinical model and a widely accepted staging grading system. Recurrence-free survival (RFS) and overall survival (OS) were also predicted in order to further assess survival benefits. Findings: DL signature showed strong power for predicting recurrence (concordance index, 0.869; area under curve, 0.889) in validation set, outperforming other models and system. In addition, we divided RFS and OS into high and low risk groups by selecting appropriate cutoff values for DL signature, and calculated cumulative recurrence risk rates for both groups. Interpretation: Our proposed DL signature shows promising potential as clinical aid for predicting postoperative recurrence risk in BCa and for stratifying the risk of RFS and OS, which can be applied to guide personalized precision therapy. Funding: There are no sources of funding for this manuscript.

Glucose transporter 3 (GLUT3) promotes lactylation modifications by regulating lactate dehydrogenase A (LDHA) in gastric cancer.

OBJECTIVES: Glucose transporter 3 (GLUT3) plays a major role in glycolysis and glucose metabolism in cancer cells. We aimed to investigate the correlation between GLUT3 and histone lactylation modification in the occurrence and progression of gastric cancer. MATERIALS AND METHODS: We initially used single-cell sequencing data to determine the expression levels of GLUT3 and lactate dehydrogenase A (LDHA) in primary tumor, tumor-adjacent normal, and metastasis tumor tissues. Immunohistochemistry analysis was conducted to measure GLUT3, LDHA, and L-lactyl levels in gastric normal and cancer tissues. Transwell and scratch assays were performed to evaluate the metastatic and invasive capacity of gastric cancer cell lines. Western blotting was used to measure L-lactyl and histone lactylation levels in gastric cancer cell lines. RESULTS: Single-cell sequencing data showed that GLUT3 expression was significantly increased in primary tumor and metastasis tumor tissues. In addition, GLUT3 expression was positively correlated with that of LDHA expression and lactylation-related pathways. Western blotting and immunohistochemistry analyses revealed that GLUT3 was highly expressed in gastric cancer tissues and cell lines. GLUT3 knockdown in gastric cancer cell lines inhibited their metastatic and invasive capacity to various degrees. Additionally, the levels of LDHA, L-lactyl, H3K9, H3K18, and H3K56 significantly decreased after GLUT3 knockdown, indicating that GLUT3 affects lactylation in gastric cancer cells. Moreover, LDHA overexpression in a GLUT3 knockdown cell line reversed the levels of lactylation and EMT-related markers, and the EMT functional phenotype induced by GLUT3 knockdown. The in vivo results were consistent with the in vitro results. CONCLUSIONS: This study suggests the important role of histone lactylation in the occurrence and progression of gastric cancer, and GLUT3 may be a new diagnostic marker and therapeutic target for gastric cancer.

Non-contrast computed tomography-based radiomics for staging of connective tissue disease-associated interstitial lung disease.

Rationale and introduction: It is of significance to assess the severity and predict the mortality of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). In this double-center retrospective study, we developed and validated a radiomics nomogram for clinical management by using the ILD-GAP (gender, age, and pulmonary physiology) index system. Materials and methods: Patients with CTD-ILD were staged using the ILD-GAP index system. A clinical factor model was built by demographics and CT features, and a radiomics signature was developed using radiomics features extracted from CT images. Combined with the radiomics signature and independent clinical factors, a radiomics nomogram was constructed and evaluated by the area under the curve (AUC) from receiver operating characteristic (ROC) analyses. The models were externally validated in dataset 2 to evaluate the model generalization ability using ROC analysis. Results: A total of 245 patients from two clinical centers (dataset 1, n = 202; dataset 2, n = 43) were screened. Pack-years of smoking, traction bronchiectasis, and nine radiomics features were used to build the radiomics nomogram, which showed favorable calibration and discrimination in the training cohort {AUC, 0.887 [95% confidence interval (CI): 0.827-0.940]}, the internal validation cohort [AUC, 0.885 (95% CI: 0.816-0.922)], and the external validation cohort [AUC, 0.85 (95% CI: 0.720-0.919)]. Decision curve analysis demonstrated that the nomogram outperformed the clinical factor model and radiomics signature in terms of clinical usefulness. Conclusion: The CT-based radiomics nomogram showed favorable efficacy in predicting individual ILD-GAP stages.

The association of serum anti-PLA2R antibody and glomerular PLA2R antigen staining with clinical manifestations and outcomes in membranous nephropathy.

BACKGROUND: Phospholipase A2 receptor (PLA2R)-associated membranous nephropathy (MN) was manifested as seropositive for PLA2R antibodies (SAb) and/or glomerular PLA2R antigens' (GAg) deposits. According to the test of SAb and GAg, PLA2R-associated MN can be divided into SAb + /GAg-, SAb-/GAg + , and SAb + /GAg + groups. The clinical characteristics and outcomes of the three groups need to be further evaluated. METHODS: 184 PLA2R-associated MN patients were enrolled. SAb was measured by enzyme-linked immunosorbent assay with a cut-off value of 14 RU/mL. GAg was detected by immunofluorescence using a paraffin section of renal biopsy samples. Clinical characteristics and the decline of eGFR were compared among the 3 groups. RESULTS: There were 33 SAb + /GAg-, 46 SAb-/GAg +, and 105 SAb + /GAg + PLA2R-associated MN patients reviewed. Clinical characteristics, such as the level of proteinuria, serum albumin, as well as eGFR, were comparable between the SAb + /GAg- and SAb + /GAg + patients. While SAb-/GAg + patients exhibited mild clinical manifestations as evidenced by higher serum albumin (P < 0.001) and lower proteinuria (p = 0.049) compared with SAb + /GAg + patients. After 21.96 ± 7.39 month follow-up, the eGFR decrease was no difference between the SAb + /GAg- and SAb + /GAg + patients. SAb-/GAg + patients had a lower rate of the > 20% eGFR decline as well as a 50% eGFR decline compared with the SAb + /GAg + patients (10.87% vs 30.48%, p = 0.013; 0.00% vs 4.76%, p = 0.324). CONCLUSIONS: Our study showed that the clinical manifestations of SAb + /Gag- patients were the same as those of double-positive patients, while SAb-/GAg + patients exhibited mild clinical manifestations and slower eGFR decline compared to the double-positive patients.

CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study.

BACKGROUND: To construct and assess a computed tomography (CT)-based deep learning radiomics nomogram (DLRN) for predicting the pathological grade of bladder cancer (BCa) preoperatively. METHODS: We retrospectively enrolled 688 patients with BCa (469 in the training cohort, 219 in the external test cohort) who underwent surgical resection. We extracted handcrafted radiomics (HCR) features and deep learning (DL) features from three-phase CT images (including corticomedullary-phase [C-phase], nephrographic-phase [N-phase] and excretory-phase [E-phase]). We constructed predictive models using 11 machine learning classifiers, and we developed a DLRN by combining the radiomic signature with clinical factors. We assessed performance and clinical utility of the models with reference to the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: The support vector machine (SVM) classifier model based on HCR and DL combined features was the best radiomic signature, with AUC values of 0.953 and 0.943 in the training cohort and the external test cohort, respectively. The AUC values of the clinical model in the training cohort and the external test cohort were 0.752 and 0.745, respectively. DLRN performed well on both data cohorts (training cohort: AUC = 0.961; external test cohort: AUC = 0.947), and outperformed the clinical model and the optimal radiomic signature. CONCLUSION: The proposed CT-based DLRN showed good diagnostic capability in distinguishing between high and low grade BCa.

Neutrophil extracellular traps promote angiogenesis in gastric cancer.

Although antiangiogenic therapy has been used in gastric cancer, disease progression due to drug resistance remains common. Neutrophils play an important role in the occurrence and progression of cancer via neutrophil extracellular traps (NETs). However, few studies have investigated angiogenic regulation in gastric cancer. We aimed to determine the role of NETs in promoting angiogenesis in gastric cancer. Multiple immunohistochemical staining was used to analyze the spatial distribution of NETs and microvessels in patient tissue samples. A mouse subcutaneous tumor model was established to determine the effect of NETs on tumor growth, and changes in microvessel density were observed via immunohistochemical staining. We screened differentially expressed proteins in HUVECs stimulated by NETs via proteomics. Cell Counting Kit-8, EdU labeling, and tubule formation assays were used to verify the effect of NETs on HUVEC proliferation, migration, and tubule formation. Blocking NETs, which was related to decreased microvessel density, significantly inhibited tumor growth in the murine subcutaneous tumor model. Compared with those of the control group, tumor volume and mass among mice in the inhibition group decreased by 61.3% and 77.9%, respectively. The NET-DNA receptor CCDC25 was expressed in HUVECs, providing a platform for NETs to promote HUVEC proliferation, migration, and tubulation. In an in vitro rat aortic explant model, NETs induced HUVEC proliferation, survival, and chemotaxis, which were not significantly different from those observed in the VEGF stimulation group. Our results confirm that NETs promote angiogenesis in gastric cancer, providing a theoretical basis for identifying new anti-vascular therapeutic targets. Video Abstract.

Large-scale and cost-effective production of recombinant human serum albumin (rHSA) in transgenic Bombyx mori cocoons.

HSA is considered a versatile natural cargo carrier with multiple bio-functions and applications. However, insufficient supply of HSA has limited widespread use. Although various recombinant expression systems had been applied to produce the rHSA to overcome the limited resource, cost-effective and large scale production of rHSA remains a challenge. Herein, we provide a strategy for the large-scale and cost-effective production of rHSA in cocoons of transgenic silkworms, achieving a final 13.54 ± 1.34 g/kg of rHSA yield in cocoons. rHSA was efficiently synthesized and stable over the long-term in the cocoons at room temperature. Artificial control of silk crystal structure during silk spinning significantly facilitated rHSA extraction and purification, with 99.69 ± 0.33 % purity and a productivity of 8.06 ± 0.17 g rHSA from 1 kg cocoons. The rHSA had the same secondary structure to natural HSA, along with effective drug binding capacity, biocompatibility, and bio-safe. The rHSA was successfully evaluated as a potential substitute in serum-free cell culture. These findings suggest the silkworm bioreactor is promising for large-scale and cost-effective production of high quality rHSA to meet the increased worldwide demand.

Multiphysics Interaction Analysis of the Therapeutic Effects of the Sigmoid Sinus Wall Reconstruction in Patients with Venous Pulsatile Tinnitus.

Sigmoid sinus wall dehiscence (SSWD) is an important etiology of venous pulsatile tinnitus (VPT) and is treated by sigmoid sinus wall reconstruction (SSWR). This study aimed to investigate the therapeutic effects of the different degrees of SSWR and the prognostic effect in patients with VPT. Personalized models of three patients with SSWD (control), 3/4SSWD, 1/2SSWD, 1/4SSWD, and 0SSWD were reconstructed. A multiphysics interaction approach was applied to elucidate the biomechanical and acoustic changes. Results revealed that after SSWR, the average pressure of venous vessel on the SSWD region reduced by 33.70 ± 12.53%, 35.86 ± 12.39%, and 39.70 ± 12.45% (mean ± SD) in three patients with 3/4SSWD, 1/2SSWD, and 1/4SSWD. The maximum displacement of the SSWR region reduced by 25.91 ± 30.20%, 37.20 ± 31.47%, 52.60 ± 34.66%, and 79.35 ± 18.13% (mean ± SD) in three patients with 3/4SSWD, 1/2SSWD, 1/4SSWD, and 0SSWD, with a magnitude approximately 10-3 times that of the venous vessel in the SSWD region. The sound pressure level at the tympanum reduced by 23.72 ± 1.91%, 31.03 ± 14.40%, 45.62 ± 19.11%, and 128.46 ± 15.46% (mean ± SD). The SSWR region was still loaded with high stress in comparison to the surrounding region. The SSWR region of the temporal bone effectively shielded the high wall pressure and blocked the transmission of venous vessel vibration to the inner ear. Patients with inadequate SSWR still had residual VPT symptoms despite the remission of VPT symptoms. Complete SSWR could completely solve VPT issues. High-stress distribution of the SSWR region may be the cause of the recurrence of VPT symptoms.

Neutrophil extracellular traps induced by the hypoxic microenvironment in gastric cancer augment tumour growth.

BACKGROUND: Inflammation-related predisposition to cancer plays an essential role in cancer progression and is associated with poor prognosis. A hypoxic microenvironment and neutrophil infiltration are commonly present in solid tumours, including gastric cancer (GC). Neutrophil extracellular traps (NETs) have also been demonstrated in the tumour immune microenvironment (TIME), but how NETs affect GC progression remains unknown. Here, we investigated the role of NET formation in the TIME and further explored the underlying mechanism of NETs in GC tumour growth. METHODS: Hypoxia-induced factor-1α (HIF-1α), citrulline histone 3 (citH3) and CD66b expression in tumour and adjacent nontumor tissue samples was evaluated by western blotting, immunofluorescence and immunohistochemical staining. The expression of neutrophil-attracting chemokines in GC cells and their hypoxic-CM was measured by qRT‒PCR and ELISA. Neutrophil migration under hypoxic conditions was evaluated by a Transwell assay. Pathway activation in neutrophils in a hypoxic microenvironment were analysed by western blotting. NET formation was measured in vitro by immunofluorescence staining. The protumour effect of NETs on GC cells was identified by Transwell, wound healing and cell proliferation assays. In vivo, an lipopolysaccharide (LPS)-induced NET model and subcutaneous tumour model were established in BALB/c nude mice to explore the mechanism of NETs in tumour growth. RESULTS: GC generates a hypoxic microenvironment that recruits neutrophils and induces NET formation. High mobility group box 1 (HMGB1) was translocated to the cytoplasm from the nucleus of GC cells in the hypoxic microenvironment and mediated the formation of NETs via the toll-like receptor 4 (TLR4)/p38 MAPK signalling pathway in neutrophils. HMGB1/TLR4/p38 MAPK pathway inhibition abrogated hypoxia-induced neutrophil activation and NET formation. NETs directly induced GC cell invasion and migration but not proliferation and accelerated the augmentation of GC growth by increasing angiogenesis. This rapid tumour growth was abolished by treatment with the NET inhibitor deoxyribonuclease I (DNase I) or a p38 MAPK signalling pathway inhibitor. CONCLUSIONS: Hypoxia triggers an inflammatory response and NET formation in the GC TIME to augment tumour growth. Targeting NETs with DNase I or HMGB1/TLR4/p38 MAPK pathway inhibitors is a potential therapeutic strategy to inhibit GC progression. Video Abstract.

Effect of NETs/COX-2 pathway on immune microenvironment and metastasis in gastric cancer.

Background: Neutrophil extracellular traps (NETs) are crucial in the progression of several cancers. The formation of NETs is closely related to reactive oxygen species (ROS), and the granule proteins involved in nucleosome depolymerization under the action of ROS together with the loosened DNA compose the basic structure of NETs. This study aims to investigate the specific mechanisms of NETs promoting gastric cancer metastasis in order to perfect the existing immunotherapy strategies. Methods: In this study, the cells and tumor tissues of gastric cancer were detected by immunological experiments, real-time polymerase chain reaction and cytology experiments. Besides, bioinformatics analysis was used to analyze the correlation between cyclooxygenase-2 (COX-2) and the immune microenvironment of gastric cancer, as well as its effect on immunotherapy. Results: Examination of clinical specimens showed that NETs were deposited in tumor tissues of patients with gastric cancer and their expression was significantly correlated with tumor staging. Bioinformatics analysis showed that COX-2 was involved in gastric cancer progression and was associated with immune cell infiltration as well as immunotherapy. In vitro experiments, we demonstrated that NETs could activate COX-2 through Toll-like receptor 2 (TLR2) and thus enhance the metastatic ability of gastric cancer cells. In addition, in a liver metastasis model of nude mice we also demonstrated the critical role of NETs and COX-2 in the distant metastasis of gastric cancer. Conclusion: NETs can promote gastric cancer metastasis by initiating COX-2 through TLR2, and COX-2 may become a target for gastric cancer immunotherapy.

Identification of lactylation related model to predict prognostic, tumor infiltrating immunocytes and response of immunotherapy in gastric cancer.

Background: The epigenetic regulatory chemical lactate is a product of glycolysis. It can regulate gene expression through histone lactylation, thereby promoting tumor proliferation, metastasis, and immunosuppression. Methods: In this study, a lactylation-related model for gastric cancer (GC) was constructed, and its relationships to prognosis, immune cell infiltration, and immunotherapy were investigated. By contrasting normal tissues and tumor tissues, four lactylation-related pathways that were substantially expressed in GC tissues were found in the GSEA database. Six lactylation-related genes were screened for bioinformatic analysis. The GC data sets from the TCGA and GEO databases were downloaded and integrated to perform cluster analysis, and the lactylation related model was constructed by secondary clustering. Results: The fingding demonstrated that the lactylation score has a strong correlation with the overall survival rate from GC and the progression of GC. Mechanistic experiments showed that abundant immune cell infiltration (macrophages showed the highest degree of infiltration) and increased genetic instability are traits of high lactylation scores. Immune checkpoint inhibitors (ICIs) demonstrated a reduced response rate in GC with high lactylation scores. At the same time, tumors with high lactylation scores had high Tumor Immune Dysfunction and Exclusion scores, which means that they had a higher risk of immune evasion and dysfunction. Discussion: These findings indicate that the lactylation score can be used to predict the malignant progression and immune evasion of GC. This model also can guide the treatment response to ICIs of GC. The constructed model of the lactate gene is also expected to become a potential therapeutic target for GC and diagnostic marker.

Impact of the immune molecular profile of the tumor microenvironment on the prognosis of NSCLC.

The present study aimed to clarify the association between macrophages, tumor neo-vessels and programmed cell death-ligand 1 (PD-L1) in the tumor microenvironment and the clinicopathological features of patients with non-small cell lung cancer (NSCLC), and to explore the prognostic factors of stromal features in NSCLC. To determine this, tissue microarrays containing samples of 92 patients with NSCLC were studied using immunohistochemistry and immunofluorescence. The quantitative data demonstrated that in tumor islets, the number of CD68+ and CD206+ tumor-associated macrophages (TAMs) was 8-348 (median, 131) and 2-220 (median, 52), respectively (P<0.001). In tumor stroma, the number of CD68+ and CD206+ TAMs was 23-412 (median, 169) and 7-358 (median, 81), respectively (P<0.001). The number of CD68+ TAMs in each location of the tumor islets and tumor stroma was significantly higher than that of CD206+ TAMs, and they were significantly correlated (P<0.0001). The quantitative density of CD105 and PD-L1 in tumor tissues was 19-368 (median, 156) and 9-493 (median, 103), respectively. Survival analysis revealed that a high density of CD68+ TAMs in tumor stroma and islets and a high density of CD206+ TAMs and PD-L1 in tumor stroma were associated with worse prognosis (both P<0.05). Collectively, the survival analysis demonstrated that the high-density group was related to a worse prognosis regardless of combined neo-vessels and PD-L1 expression with the CD68+ TAMs in tumor islets and stroma, or CD206+ TAMs in tumor islets and stroma. To the best of our knowledge, the present study was the first to provide a multi-component combined prognostic survival analysis of different types of macrophages in different regions with tumor neo-vessels and PD-L1, which demonstrated the importance of macrophages in tumor stroma.

A novel computed tomography angiography technique: guided preoperative localization and design of anterolateral thigh perforator flap.

BACKGROUND: Anterolateral thigh perforator (ALTP) flap is considered a versatile flap for soft tissue reconstruction. Computed tomography angiography (CTA) is used for mapping perforator in abdominal-based reconstruction; however, it is less commonly used in ALTP due to its poor imaging efficacy. In this study, we introduced a novel CTA technique for preoperative localization and design of ALTP flap and evaluated its value in directing surgical reconstruction. RESULTS: Thirty-five patients with soft tissue defects were consecutively enrolled. Modified CTA procedures, such as sharp convolution kernel, ADMIRE iterative reconstruction, 80 kV tube voltage, high flow contrast agent and cinematic rendering image reconstruction, were used to map ALTPs. A total of 287 perforators (including 884 sub-branches) were determined, with a mean of 5 perforators per thigh (range 2-11). The ALTPs were mainly concentrated in the "hot zone" (42%, 121/287) or the distal zone (41%, 118/287). Most perforators originated from the descending branch of the lateral circumflex femoral artery (76%, 219/287). Three perforator types, namely musculocutaneous (62%, 177/287), septocutaneous (33%, 96/287), and mixed pattern (5%, 14/287), were identified. The median pedicle length measured by two methods was 4.1 cm (range 0.7-20.3 cm) and 17.0 cm (range 4.7-33.9 cm), respectively, and the median diameter of the skin flap nourished by one perforator was 3.4 cm (IQR 2.1-5.7 cm). Twenty-eight ALTP flaps were obtained with the guidance of CTA, and 26 flaps survived after follow-up. CONCLUSIONS: The proposed CTA mapping technique is a useful tool for preoperative localization and design of ALTP flap.